House Sales Prices and Venues in Municipal Parts of Prague

A.Introduction

Prague is one of the most popular city in Europe with many outstanding views and historic, exciting structures. My journey of Prague has started with the hiring of my wife by a global company there. In this study, I give a brief insight about the population, house prices and types and compare venues in different municipal parts of the city for anyone looking for buying a property in Prague or interested in real estate industry.

B.Data Prepareation & Description

The data is used in this project can be divided into three categories.

-Population by Municipal Parts: I took the table from ... as csv.

-House sales price: I scrape all available advertisements for Prague in SReality.com which is one the most used webpage for property sale. (around 4700 objects)

-Venues data in municipal parts: I used Foursquare API for getting the data.

B1.Population by Municipal Parts

Here is the population data for 57 Municipal Parts of Prague.

• Resource:https://www.citypopulation.de/en/czechrep/praguecity/

#Import libraries
import pandas as pd
import numpy as np

df_pra_pop = pd.read_csv("Prague_population.csv")
df_pra_pop

	Districts	Population
0	Praha 1	29563
1	Praha 2	50363
2	Praha 3	76041
3	Praha 4	132068
4	Praha 5	88225
5	Praha 6	107213
6	Praha 7	45226
7	Praha 8	106218
8	Praha 9	60601
9	Praha 10	110571
10	Praha 11	77324
11	Praha 12	57821
12	Praha 13	63554
13	Praha 14	47761
14	Praha 15	34720
15	Praha 16	8694
16	Praha 17	24075
17	Praha 18	21516
18	Praha 19	7326
19	Praha 20	15652
20	Praha 21	10860
21	Praha 22	12559
22	Bechovice	2733
23	Benice	704
24	Brezineves	1754
25	Cakovice	11868
26	Dablice	3647
27	Dolni Chabry	4801
28	Dolni Mecholupy	3314
29	Dolni Pocernice	2709
30	Dubec	4023
31	Klanovice	3757
32	Kolodeje	1580
33	Kolovraty	3912
34	Kralovice	407
35	Kreslice	1117
36	Kunratice	10023
37	Libus	10623
38	Lipence	2911
39	Lochkov	828
40	Lysolaje	1509
41	Nebusice	3372
42	Nedvezi	333
43	Petrovice	6035
44	Predni Kopanina	693
45	Reporyje	5205
46	Satalice	2551
47	Seberov	3309
48	Slivenec	3696
49	Sterboholy	2367
50	Suchdol	7225
51	Troja	1426
52	Ujezd	3498
53	Velka Chuchle	2532
54	Vinor	4557
55	Zbraslav	10049
56	Zlicin	7258

B2.House sales price

Sreality.com is one of the most used advirtisement webpage for house sale. I scraped the data by using chromedriver, and renamed and dropped some of the columns. This part took high amounth time. At the end I have the data frame below. If you want to look how I scraped the data from the page you may look Scraping.ipynb.

-Resource: https://www.sreality.cz/

• Title: Advertisement title
• Type: Type of the house: 1+kk,2+1,etc.(I'll explain the meanin of kk further)
• Area: Area of the house in m2
• Latitude: The latitude of the house location
• Longititude: The longititude of house location
• Coodinates: Combined latitude and longitude
• Price(CZK): Price of the house in CZK

Here I would like to decribe type of the houses in Czech Republic. The naming could be different than other countries.

• 1+1: one room and one seperated kitchen
• 1+kk: one room and the kitchen is inside room
• 2+1: two rooms and one seperated kitchen
• 2+kk: two rooms and the kitchen is inside one of the room

df=pd.read_csv('prague_house_prices_v1.csv')
df.head()

	Title	Type	Area	Latitude	Longitude	Coordinates	Price(CZK)
0	Byt 1+kk 43 m² k prodeji Hugo Haase, Praha 5 -...	1+kk	43	50.0290	14.3622	(50.029, 14.3623)	4274096
1	Byt 3+kk 75 m² k prodeji Na Veselí, Praha 4 - ...	3+kk	75	50.0560	14.4414	(50.056, 14.4414)	7990000
2	Byt 2+1 71 m² k prodeji Rooseveltova, Praha 6 ...	2+1	71	50.1036	14.3994	(50.1036, 14.3995)	8900000
3	Byt 1+kk 27 m² k prodeji Střešovická, Praha 6 ...	1+kk	27	50.0930	14.3773	(50.0931, 14.3773)	3299000
4	Byt 4+kk 100 m² k prodeji 5. května, Praha 4 -...	4+kk	100	50.0610	14.4318	(50.061, 14.4318)	9490000

B3.Venues data in municipal parts

I used Foursquare API for getting the data.

C.Methodology

C.1.Municipal Parts of Prague

In this section, I created a foluim map to observe Prague Municipal Parts and created a gif for my blog.

Plain Prague Map

#import libs
import wget
import folium

#json
prague_regions_map = r'TMMESTSKECASTI_P1.json' # geojson file

# create a plain Prague map
prague_regions = folium.Map(location=[50.06, 14.45], zoom_start=11)
prague_regions

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Prague Municipal Parts

You can scroll around map to observe municipal parts.

#dumy data for region gif
df_pop_new=df_pra_pop

df_pop_new['Population'] =5

# create a plain world map
communities_map = folium.Map(location=[50.06, 14.45], zoom_start=11, tiles='stamenwatercolor')

# generate choropleth map 
choropleth = folium.Choropleth(
    geo_data=prague_regions_map,
    data=df_pop_new,
    columns=['Districts', 'Population'],
    key_on='feature.properties.NAZEV_1',
    fill_color='YlOrBr', 
    fill_opacity=1, 
    line_opacity=1,
    highlight=True,
    smooth_factor=0).add_to(communities_map)

# add labels indicating the name of the community
style_function = "font-size: 15px; font-weight: bold"
choropleth.geojson.add_child(
    folium.features.GeoJsonTooltip(['NAZEV_1'], style=style_function, labels=False))


communities_map.save('map_for_gif.html')

# display map
communities_map

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Population by Municipal Part

#retrieve data
df_pra_pop = pd.read_csv("Prague_population.csv")

prague_regions_map = r'TMMESTSKECASTI_P1.json'

# let Folium determine the scale.
prague_regions = folium.Map(location=[50.06, 14.45], zoom_start=11, tiles='stamenwatercolor')
prague_regions.choropleth(
    geo_data=prague_regions_map,
    data=df_pra_pop,
    columns=['Districts', 'Population'],
    key_on='feature.properties.NAZEV_1',
    fill_opacity=0.9, 
    line_opacity=0.5,
    legend_name='Population in Prague',
    highlight=True,
    reset=True)


prague_regions

c:\users\taner\appdata\local\programs\python\python38\lib\site-packages\folium\folium.py:411: FutureWarning: The choropleth  method has been deprecated. Instead use the new Choropleth class, which has the same arguments. See the example notebook 'GeoJSON_and_choropleth' for how to do this.
  warnings.warn(

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Praha 4 and 10 are the most crowded municipal parts of Prague.

C.2.House Sale Prices

Retrieve the House Sale Prices data.

df=pd.read_csv('prague_house_prices_v1.csv')

df.head()

	Title	Type	Area	Latitude	Longitude	Coordinates	Price(CZK)
0	Byt 1+kk 43 m² k prodeji Hugo Haase, Praha 5 -...	1+kk	43	50.0290	14.3622	(50.029, 14.3623)	4274096
1	Byt 3+kk 75 m² k prodeji Na Veselí, Praha 4 - ...	3+kk	75	50.0560	14.4414	(50.056, 14.4414)	7990000
2	Byt 2+1 71 m² k prodeji Rooseveltova, Praha 6 ...	2+1	71	50.1036	14.3994	(50.1036, 14.3995)	8900000
3	Byt 1+kk 27 m² k prodeji Střešovická, Praha 6 ...	1+kk	27	50.0930	14.3773	(50.0931, 14.3773)	3299000
4	Byt 4+kk 100 m² k prodeji 5. května, Praha 4 -...	4+kk	100	50.0610	14.4318	(50.061, 14.4318)	9490000

Observe the house types.

df['Type'].value_counts()

2+kk        1212
3+kk         984
1+kk         625
3+1          444
4+kk         434
2+1          271
4+1          136
5+kk         104
1+1           93
atypicky      35
5+1           31
6-a-vice      21
Name: Type, dtype: int64

Make latitude and longitude numeric

df['Latitude'] = pd.to_numeric(df['Latitude'],errors='coerce')
df['Longitude'] = pd.to_numeric(df['Longitude'],errors='coerce')

df.dtypes

Title           object
Type            object
Area             int64
Latitude       float64
Longitude      float64
Coordinates     object
Price(CZK)       int64
dtype: object

Check number of objects (advertisement)

df.head(20)
df.shape

(4390, 7)

Check NaN values

df['Latitude'].isnull().sum()

4

df['Longitude'].isnull().sum()

2

#find missing values
df[df.isnull().any(axis=1)]

	Title	Type	Area	Latitude	Longitude	Coordinates	Price(CZK)
567	Byt 1+kk 32 m² k prodeji Františka Jansy, Prah...	1+kk	32	NaN	14.5527	(50.065, 14.5527)	1199000
1917	Byt 2+kk 101 m² k prodeji Na Ořechovce, Praha ...	2+kk	101	50.0953	NaN	(50.0953, 14.381)	11990000
2077	Byt 3+kk 93 m² k prodeji Františka Jansy, Prah...	3+kk	93	NaN	14.5527	(50.065, 14.5527)	2850000
2434	Byt 3+kk 101 m² k prodeji Na Ořechovce, Praha ...	3+kk	101	50.0953	NaN	(50.0953, 14.381)	11990000
2480	Byt 1+kk 27 m² k prodeji Bajkonurská, Praha 4 ...	1+kk	27	NaN	14.5286	(50.03, 14.5286)	2690000
3669	Byt 1+kk 30 m² k prodeji Jurkovičova, Praha 4 ...	1+kk	30	NaN	14.5296	(50.027, 14.5296)	3350000

Correct NaN values

#Correct values
df.iloc[567,3] = 50.065
df.iloc[1917,4] = 14.381
df.iloc[2077,3] = 50.065
df.iloc[2434,4] = 14.381
df.iloc[2480,3] = 50.03
df.iloc[3669,3] = 50.027

#find missing values
df[df.isnull().any(axis=1)]

	Title	Type	Area	Latitude	Longitude	Coordinates	Price(CZK)

Add region column to the dataframe by using json file

#!pip install shapely
import shapely
import geojson

import json
from shapely.geometry import shape, GeometryCollection, Point
region_list=[]

with open('TMMESTSKECASTI_P1.json', 'r') as f:
    js = json.load(f)

for i in range(0,4390):
    x= df['Longitude']
    y= df['Latitude']
    x=x.iloc[i]
    y=y.iloc[i]
    point = Point(x, y)
    for feature in js['features']:
        polygon = shape(feature['geometry'])
        if polygon.contains(point):
            region_list.append(str(feature['properties']['NAZEV_1']))

df_region=pd.DataFrame(region_list)

df_region.shape

(4390, 1)

df['Region'] =df_region

Now we have Region column

df.head()

	Title	Type	Area	Latitude	Longitude	Coordinates	Price(CZK)	Region
0	Byt 1+kk 43 m² k prodeji Hugo Haase, Praha 5 -...	1+kk	43	50.0290	14.3622	(50.029, 14.3623)	4274096	Praha 5
1	Byt 3+kk 75 m² k prodeji Na Veselí, Praha 4 - ...	3+kk	75	50.0560	14.4414	(50.056, 14.4414)	7990000	Praha 4
2	Byt 2+1 71 m² k prodeji Rooseveltova, Praha 6 ...	2+1	71	50.1036	14.3994	(50.1036, 14.3995)	8900000	Praha 6
3	Byt 1+kk 27 m² k prodeji Střešovická, Praha 6 ...	1+kk	27	50.0930	14.3773	(50.0931, 14.3773)	3299000	Praha 6
4	Byt 4+kk 100 m² k prodeji 5. května, Praha 4 -...	4+kk	100	50.0610	14.4318	(50.061, 14.4318)	9490000	Praha 4

df.tail()

	Title	Type	Area	Latitude	Longitude	Coordinates	Price(CZK)	Region
4385	Byt 2+kk 58 m² k prodeji Tlumačovská, Praha 5 ...	2+kk	58	50.0399	14.3357	(50.0399, 14.3358)	5850000	Praha 13
4386	Byt 4+kk 126 m² k prodeji Praha 5; 9 989 000 K...	4+kk	126	50.0835	14.4341	(50.0835, 14.4341)	9989000	Praha 1
4387	Byt 1+kk 35 m² k prodeji Olšanská, Praha 3 - Ž...	1+kk	35	50.0833	14.4662	(50.0834, 14.4662)	4290000	Praha 3
4388	Byt 4+kk 140 m² k prodeji Na pomezí, Praha 5 -...	4+kk	140	50.0614	14.3663	(50.0615, 14.3663)	21679000	Praha 5
4389	Byt 1+kk 39 m² k prodeji Grafická, Praha 5 - S...	1+kk	39	50.0730	14.3945	(50.0731, 14.3945)	5990000	Praha 5

Drop Title column

df.drop(['Title'], axis=1, inplace=True)

df.head()

	Type	Area	Latitude	Longitude	Coordinates	Price(CZK)	Region
0	1+kk	43	50.0290	14.3622	(50.029, 14.3623)	4274096	Praha 5
1	3+kk	75	50.0560	14.4414	(50.056, 14.4414)	7990000	Praha 4
2	2+1	71	50.1036	14.3994	(50.1036, 14.3995)	8900000	Praha 6
3	1+kk	27	50.0930	14.3773	(50.0931, 14.3773)	3299000	Praha 6
4	4+kk	100	50.0610	14.4318	(50.061, 14.4318)	9490000	Praha 4

Export clean data

#export 
df.to_csv('Prague_houses.csv', index=False)

Exclude the municipal parts having advertisements less than 17

#exclude the regions have objects less than 10
s = df['Region'].value_counts()
df_new = df[df.isin(s.index[s >= 17]).values]

#Average price by region
df_grouped=df_new.groupby('Region').mean()
df_grouped_price=df_grouped[['Price(CZK)']]
df_grouped_area=df_grouped[['Area']]

#sort
df_grouped_price.sort_values(['Price(CZK)'], ascending=False, axis=0, inplace=True)
df_grouped_area.sort_values(['Area'], ascending=False, axis=0, inplace=True)
#get the top 7 entries
df_price_top6 = df_grouped_price.head(6)
df_area_top6 = df_grouped_area.head(6)

<ipython-input-33-a0c47cd52c7a>:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_grouped_price.sort_values(['Price(CZK)'], ascending=False, axis=0, inplace=True)
<ipython-input-33-a0c47cd52c7a>:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_grouped_area.sort_values(['Area'], ascending=False, axis=0, inplace=True)

# use the inline backend to generate the plots within the browser
%matplotlib inline 

import matplotlib as mpl
import matplotlib.pyplot as plt

mpl.style.use('ggplot') # optional: for ggplot-like style

# check for latest version of Matplotlib
print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0

Matplotlib version:  3.3.2

# step 2: plot data
df_price_top6.plot(kind='bar', figsize=(10, 6))

plt.xlabel('Region') # add to x-label to the plot
plt.ylabel("Price in million CZK") # add y-label to the plot
plt.title('Average House Prices by Regions in Prague') # add title to the plot

#Axes.ticklabel_format(self, *, axis='both', style='', scilimits=None, useOffset=None, useLocale=None, useMathText=None)
plt.ticklabel_format(scilimits=(6,6), axis='y')

plt.show()

The houses in Praha 1 and Praha 6 are more expensive than others.

# step 2: plot data
df_area_top6.plot(kind='bar', figsize=(10, 6), color='orange')

plt.xlabel('Region') # add to x-label to the plot
plt.ylabel("Area per house (m2)") # add y-label to the plot
plt.title('Average Area per House in Prague Regions ') # add title to the plot

plt.show()

The houses in Praha 1 and Praha 6 are larger than others.

df_grouped_type=df.groupby('Type').count()
df_type=df_grouped_type[['Area']]
df_type.rename(columns={'Area' : 'Count'}, inplace=True)

c:\users\taner\appdata\local\programs\python\python38\lib\site-packages\pandas\core\frame.py:4300: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  return super().rename(

df_type.sort_values(['Count'], ascending=False, axis=0, inplace=True)
df_type=df_type.head(5)

<ipython-input-38-76b43bdfd13d>:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_type.sort_values(['Count'], ascending=False, axis=0, inplace=True)

# step 2: plot data
df_type.plot(kind='bar', figsize=(10, 6), color='green')

plt.xlabel('Type of the House') # add to x-label to the plot
plt.ylabel("Number of House") # add y-label to the plot
plt.title('Number of House by Type in Prague') # add title to the plot

plt.show()

2+kk and 3+kk is most prevelant house types in Prague

Create a data frame for most expensive 20 houses in Prague

df_incidents = df.sort_values(['Price(CZK)'], ascending=False, axis=0, inplace=False).head(20)
df_incidents

	Type	Area	Latitude	Longitude	Coordinates	Price(CZK)	Region
290	5+kk	235	50.0816	14.4137	(50.0817, 14.4138)	79000000	Praha 1
27	5+kk	235	50.0833	14.4139	(50.0833, 14.4139)	79000000	Praha 1
3358	5+kk	235	50.0816	14.4137	(50.0817, 14.4138)	79000000	Praha 1
1142	atypicky	445	50.0865	14.4323	(50.0866, 14.4323)	59870000	Praha 1
3398	4+kk	255	50.0903	14.4216	(50.0904, 14.4217)	54450000	Praha 1
358	4+kk	229	50.0845	14.4177	(50.0846, 14.4178)	50000000	Praha 1
3270	5+kk	302	50.0614	14.3663	(50.0615, 14.3663)	49849000	Praha 5
2889	4+kk	169	50.0891	14.4183	(50.0892, 14.4184)	49000000	Praha 1
1769	6-a-vice	243	50.0730	14.3889	(50.0731, 14.389)	45990000	Praha 5
942	4+kk	315	50.0845	14.4177	(50.0846, 14.4178)	45000000	Praha 1
1969	4+kk	256	50.0845	14.4177	(50.0846, 14.4178)	45000000	Praha 1
1068	6-a-vice	318	50.0092	14.3858	(50.0093, 14.3858)	42025000	Velka Chuchle
591	3+1	329	50.0865	14.4323	(50.0866, 14.4323)	41928000	Praha 1
974	4+kk	329	50.0865	14.4323	(50.0866, 14.4323)	41928000	Praha 1
3483	atypicky	388	50.0828	14.3764	(50.0828, 14.3765)	39900000	Praha 6
2605	6-a-vice	241	50.0735	14.4629	(50.0735, 14.463)	39890000	Praha 10
2877	3+1	135	50.0878	14.4153	(50.0878, 14.4153)	38000000	Praha 1
1750	4+1	226	50.0981	14.4706	(50.0981, 14.4707)	37990000	Praha 8
4139	5+kk	209	50.0770	14.3876	(50.0771, 14.3877)	37550000	Praha 5
1967	4+kk	211	50.0795	14.4139	(50.0795, 14.414)	37030007	Praha 1

prague_regions_v2 = folium.Map(location=[50.06, 14.45], zoom_start=11, tiles='stamenwatercolor')

prague_regions_v2.choropleth(
    geo_data=prague_regions_map,
    key_on='feature.properties.NAZEV_1',
    fill_opacity=0.9, 
    line_opacity=0.5,
    highlight=True,
    fill_color='yellow',
    legend_name=None,
    reset=True)


incidents = folium.map.FeatureGroup()

# loop through the 100 crimes and add each to the incidents feature group
for lat, lng, in zip(df_incidents.Latitude, df_incidents.Longitude):
    incidents.add_child(
        folium.CircleMarker(
            [lat, lng],
            radius=5, # define how big you want the circle markers to be
            color='red',
            fill=True,
            fill_color=None,
            fill_opacity=0.3
        )
    )

# add incidents to map
prague_regions_v2.add_child(incidents)

c:\users\taner\appdata\local\programs\python\python38\lib\site-packages\folium\folium.py:411: FutureWarning: The choropleth  method has been deprecated. Instead use the new Choropleth class, which has the same arguments. See the example notebook 'GeoJSON_and_choropleth' for how to do this.
  warnings.warn(

Make this Notebook Trusted to load map: File -> Trust Notebook

The most expensive houses are mainly placed in Praha 1.

Regression

I will look for correlation between price and other factors in this section.

C.3.Compare Municipal Parts

I will compare and cluster region in this section.

D.4.Results and Discussion

E.4.Conclusion